Dataset: Retail Sales Data

About this Dataset

Retail Sales Data

Content

This data is collected from a Turkish retail company. Time period is beginning from 2017 to the end of 2019.

File descriptions

sales.csv - Train data. Daily sales data covering 2017-2019.
product_hierarchy.csv - Data containing the hierarchy and sizes of the products.
store_cities.csv - Data containing the city, type and size information of the stores.

Column descriptions

store_id - The unique identifier of a store.
product_id - The unique identifier of a product.
date - Sales date (YYYY-MM-DD)
sales - Sales quantity
revenue - Daily total sales revenue
stock - End of day stock quantity
price - Product sales price
promo_type_1 - Type of promotion applied on channel 1
promo_bin_1 - Binned promotion rate for applied promo_type_1
promo_type_2 - Type of promotion applied on channel 2
promo_bin_2 - Binned promotion rate for applied promo_type_2
promo_discount_2 - Discount rate for applied promo type 2
promo_discount_type_2 - Type of discount applied
product_length - Length of product
product_depth - Depth of product
product_width - Width of product
hierarchy1_id
hierarchy2_id
hierarchy3_id
hierarchy4_id
hierarchy5_id
storetype_id
store_size
city_id
train_or_test - rows with train tag will be used to train KNNRegressor and rows with test tag will be used for accuracy calculation

Tables

Product Hierarchy

@kaggle.berkayalan_retail_sales_data.product_hierarchy

21.47 KB
699 rows
10 columns


CREATE TABLE product_hierarchy (
  "product_id" VARCHAR,
  "product_length" DOUBLE,
  "product_depth" DOUBLE,
  "product_width" DOUBLE,
  "cluster_id" VARCHAR,
  "hierarchy1_id" VARCHAR,
  "hierarchy2_id" VARCHAR,
  "hierarchy3_id" VARCHAR,
  "hierarchy4_id" VARCHAR,
  "hierarchy5_id" VARCHAR
);

Store Cities

@kaggle.berkayalan_retail_sales_data.store_cities

4.55 KB
144 rows
4 columns


CREATE TABLE store_cities (
  "store_id" VARCHAR,
  "storetype_id" VARCHAR,
  "store_size" BIGINT,
  "city_id" VARCHAR
);